The UBC Centre for Molecular Medicine and Therapeutics based at the BC Children’s Hospital Research Institute is home to a highly collaborative community of scientists connected by a common commitment to use leading edge molecular methods to advance development of therapeutics for human disease. With a strong history in neurogenetics and metabolism research, the CMMT offers one of the premier research environments in Canada for interdisciplinary biomedical research.
The Wasserman laboratory creates and applies bioinformatics methods for the study of the human genome. Research projects span the development of machine learning methods and algorithms for the detection of features in genomics data, the application of bioinformatics methods in applied projects such as the identification of genetic sequence variants causing rare disease or the design of gene therapy vectors. The lab members possess expertise spanning disciplines from mathematics to computer science and from human genetics to biochemistry. The lab’s success is highly dependent upon collaborative interactions, both internally between members and externally with research partners.
Using modern genomics data such as ATAC-seq, scRNA-seq or ChIP-seq, research in the lab addresses the core question of how the hundreds of phenotypically diverse types of cells in the human body can be generated from the same underlying DNA sequence. Current work in the lab includes studies of
X-inactivation, pacreatic beta cell differentiation, gene therapy promoter design and the detection of genetic alterations in transcription factor binding sites.
All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority.
The Research Associate will have 3+ years of experience in bioinformatics, ideally with experience pertinent to the analysis and storage of confidential genetics-related data. The ideal candidate will have experience in the implementation of databases (either SQL or MongoDB), and the coding of software using C++, Java or Python. It is expected that this individual will have demonstrated leadership and training skills, and the ability to work with people of diverse perspectives.
The candidate should have demonstrated experience in publishing research findings in scientific journals, developing funding applications (e.g. for scholarships), presenting their research findings at scientific conferences and working in collaborative scientific teams.
- Oversee the daily activities of trainees and direct a portion of the effort of a scientific programmer
- Oversee software development requiring programming in Java, C++, or Python
- Apply software for the analysis of single cell and RNA-seq transcript data
- Organize, reformat and analyze gene regulation data
- Develop machine learning methods for the analysis of cis-regulatory sequences
- Data management and tracking of NGS data
- Contribute to quarterly and annual reports related to research projects
- Work with the PI to ensure that all scientific milestones are achieved
- Keeping up-to-date with scientific advances (e.g. relevant literature review, scientific meetings, twitter)
- Present findings at local, national and international scientific meetings where the impression can have direct influence on future scientific funding
- Write manuscripts describing the findings
- Assist in the development of grant and scholarship applications related to the research
- Provide supervision and mentorship to bioinformatics trainee(s)
- Perform other related duties.
Education and Experience:
- Ph.D. degree in a life or quantitative sciences discipline with 5+ years of postdoctoral experience
- A minimum of 3+ years of experience in bioinformatics with a focus on genome sequence data processing and analysis.
Skills and Abilities:
- Knowledge of bioinformatics, with an emphasis on genome sequence analysis and gene annotation
- Knowledge of scientific tools, technologies and online resources for gene-disease relationship prediction
- Python, Java or C++ programming skills
- Experience with the Linux environment, computer clusters, SQL or MongoDB database systems
- Experience with machine learning, such as logistic regression analysis, deep learning (particularly using convolution neural networks) or random forrest classifiers
- Motif analysis in biopolymer sequences, such as the prediction of transcription factor binding sites using position weight matrices or hidden Markov models
- Analysis of single cell genomics data
- Analysis of epigenetics, transcriptomic and chromatin immunoprecipitation data.
- Use of project management tools for scientific interaction, such as wikis or shared document repositories (e.g. Git)
- Drive and enthusiasm to both lead and work as a member of a team
- Creativity, initiative and good judgment for multi-tasking in a deadline-oriented environment
- Effective oral and written communication, analytical, and interpersonal skills
- Excellent organizational skills and ability to learn new skills quickly
- Enthusiasm for highly interdisciplinary research and dedication to explore emerging techniques
- Accuracy and attention to details
HOW TO APPLY
Please email your cover letter and resume to email@example.com. Due to the number of resumes we receive, we are unable to confirm receipt of submissions over the phone, or provide the status of competitions except to those who are selected for an interview.
Equity and diversity are essential to academic excellence. An open and diverse community fosters the inclusion of voices that have been underrepresented or discouraged. We encourage applications from members of groups that have been marginalized on any grounds enumerated under the B.C. Human Rights Code, including sex, sexual orientation, gender identity or expression, racialization, disability, political belief, religion, marital or family status, age, and/or status as a First Nation, Metis, Inuit, or Indigenous person. All qualified candidates are encouraged to apply; however, Canadians and permanent residents of Canada will be given priority.